Comparison to Xarray
Imports
import matplotlib.pyplot as plt
import uxarray as ux
import xarray as xr
fig_size = 400
plot_kwargs = {"backend": "matplotlib",
"aspect": 2,
"fig_size": fig_size}
Data
Structured
base_path = "../../meshfiles/"
ds_path = base_path + "outCSne30.structured.nc"
xrds = xr.open_dataset(ds_path)
xrds
<xarray.Dataset> Size: 30kB
Dimensions: (lat: 45, lon: 80)
Coordinates:
* lat (lat) int64 360B -90 -86 -82 -78 -74 -70 -66 ... 66 70 74 78 82 86
* lon (lon) float64 640B -180.0 -175.5 -171.0 ... 166.5 171.0 175.5
Data variables:
psi (lat, lon) float64 29kB ...Unstructured
base_path = "../../meshfiles/"
grid_filename = base_path + "outCSne30.grid.ug"
data_filename = base_path + "outCSne30.data.nc"
uxds = ux.open_dataset(grid_filename, data_filename)
uxds
<xarray.UxDataset> Size: 43kB
Dimensions: (n_face: 5400)
Dimensions without coordinates: n_face
Data variables:
psi (n_face) float64 43kB 1.351 1.331 1.31 1.289 ... 0.6909 0.67 0.6495Visualization
Xarray
xrds["psi"].plot(figsize=(12, 5), cmap="inferno")
<matplotlib.collections.QuadMesh at 0x7f287c3334f0>
fig, axs = plt.subplots(nrows=2, figsize=(12, 10))
xrds["psi"].plot(cmap="inferno", ax=axs[0])
xrds["psi"].plot(cmap="cividis", ax=axs[1])
<matplotlib.collections.QuadMesh at 0x7f287b1f37c0>
UXarray
todo
uxds["psi"].plot(width=1000, height=500, cmap="inferno")
Since Xarray’s plotting functionality is written using Matplotlib we can use the matplotlib backend in UXarray to obtain a better match.
Additionally, we can rasterize our polygons instead of plotting them directly.
ADD ADMONATION TO OTHER SECTION
uxds["psi"].plot.rasterize(method='polygon', cmap="inferno", **plot_kwargs)
(
uxds["psi"].plot.rasterize(method='polygon', cmap="inferno", **plot_kwargs)
+ uxds["psi"].plot.rasterize(method='polygon', cmap="cividis", **plot_kwargs)
).opts(fig_size=fig_size).cols(1)
Xarray with hvPlot
One of the primary drawbacks to UXarray’s use of HoloViews for visualization is that there is no direct way to integrate plots generated with Xarray and UXarray together. This can be alleviated by using the hvPlot library, specifically hvplot.xarray, on Xarray’s data structures.
import holoviews as hv
import hvplot.xarray
hv.extension("bokeh")
By using xrds.hvplot() as opposed to xrds.plot(), we can create a simple figure showcasing our Structured Grid figure from Xarray and Unstructured Grid figure from UXarray in a single plot.
hv.extension("bokeh")
(
xrds.hvplot(cmap="inferno", title="Xarray with hvPlot", width=1000, height=500)
+ uxds["psi"].plot(cmap="inferno", title="UXarray Plot", width=1000, height=500)
).cols(1)
If we are not interested in the outlines of each polygon in our Unstructured Grid, we can create a raster plot, which will better match the result produced by Xarray.
ADD NOTE ABOUT POLYGON SECTION
hv.extension("matplotlib")
(
xrds.hvplot(cmap="inferno", title="Xarray with hvPlot", aspect=2)
+ uxds["psi"].plot.rasterize(method='polygon', cmap="inferno", title="UXarray Plot", **plot_kwargs)
).opts(fig_size=fig_size).cols(1)
In addition to using hvPlot, the user could also use HoloViews or Datashader on Xarray’s data structures, but this cookbook will not go into detail.
ADD LINK TO EXAMPLES?